LLM Anonymization Against Agentic Re-Identification
Agentic LLMs with web search change the threat model for text anonymization: weak contextual cues can become cross-referenceable evidence for re-identification, yet those same details also carry downstream analytic value of the text. Existing defenses either remove explicit identifiers, perturb text for formal privacy, or test rewritten text against non-web inference models, leaving underexplored the operating region between resistance to agentic web-search re-identification and utility retention. We introduce AURA (\textbf{A}nonymization with \textbf{U}tility-\textbf{R}etention \textbf{A}daptation), an LLM-powered \textit{mask-reconstruct} framework that decouples privacy localization fr...
| Authors | Ziwen Li, Jianing Wen, Tianshi Li |
|---|---|
| Primary category | cs.CR |
| Published | 29 May 2026 |
| Last updated at source | 1 October 2026 |
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